Systems | Development | Analytics | API | Testing

According to SmartBear’s Closing the AI Software Quality Gap study, 93% of teams are already using AI to generate code. The same study found that 60% expect AI to produce nearly half of all code within the next year. This shift in development velocity is already impacting software testing and quality. Most teams say application quality is suffering, and 60% have experienced quality issues in the past year because development is moving faster than testing can keep up.

We are launching with this post a new series of blog articles and LinkedIn posts titled "Features Sitting Idle". In this series, we explore key features of OctoPerf that are either misused, misunderstood, or simply unknown to our users. It's time to shine a light on these hidden gems, features that are already there, ready to become a central part of how you test. This is probably the most common situation after a load test.

Lending has always been at the heart of banking. But the way loans originated is going through a quiet but powerful shift. Customers today expect instant decisions. Not in days. Not even in hours. They expect approvals in minutes, sometimes seconds. And they expect this experience to be smooth across mobile apps, web platforms, and embedded finance ecosystems. This is where the cracks in traditional systems start to show. Legacy platforms were never designed for this kind of speed or scale.

AI accelerated software creation. It did not accelerate software trust. QA fragmentation shows up as delayed releases, engineers doing reconciliation work, and a release-readiness signal nobody fully trusts at the moment it matters most.

Not long ago, the answer to who writes tests was simple: the quality assurance (QA) engineer does. They sat downstream of development, received a build, and translated requirements into scripts. It was a defined role with a defined output. That clarity is gone. In 2026, the person or system responsible for test creation might be a business analyst (BA) mapping out a customer journey, an AI agent expanding test coverage overnight, or a QA engineer who hasn’t written a traditional script in months.

Choosing the right QA test management software is one of the highest-leverage decisions a QA team makes in 2026. If your team is still managing tests through spreadsheets or a tool that doesn't connect to your dev stack, you're leaving real coverage gaps and release time on the table.

We’ve been building an AI agent that can take a production bug, find the root cause in captured traffic, write a fix, and validate it before a human reviews it. We call it Agent Factory. Last week we ran it on ourselves, against a real bug in our own production service. The first thing we did was get the workflow wrong.

This is Part 2 of the AI Software Factory series. In Part 1, we established that the Agile methodology is buckling under the weight of “elastic code.” When AI agents can generate functionality in seconds, two-week sprints and manual task management become organizational bottlenecks. We introduced the concept of the AI Software Factory: a shift from managing human tasks to managing business intent through a “Funnel of Increasing Trust.” But a factory requires infrastructure.